Controlled release formulations can improve patient adherence and drug safety or even enable new types of basic medical research. The key to realizing these benefits is the ability to design and produce these formulations in a cost-effectiv and timely manner. Thus far, strategies at improving this process have focused on the development of novel materials and production processes that tailored for experimentally tuning a formulation's performance. Recently, the University of Pittsburgh (Pitt) has discovered new in silico design techniques that may, for the first time, make it possible to transform an expensive and time consuming empirical development process into a rapid and cost effective process. ChroKnow's long-term objective is to bring speed and efficiency formulation design process, a crucial step toward the enabling widespread adoption of controlled release systems by pharmaceutical companies and even academic laboratories. We hypothesize that algorithms (developed at Pitt) can be used to predictively design and build a diverse set of controlled release formulations based on unmet needs pharmaceutical scientists or academic researchers over a period of time that is yet unprecedented in the field. This hypothesis is supported by in vitro data demonstrating that two representative formulations have successfully been designed and produced using ChroKnow's algorithms. Herein, we put forth three real-world challenges to complete this validation: Specific Aim 1: To design and build a formulation that delivers Genentech's ranibizumab for 3 months. Motivation: Dramatically improve adherence in patients with ARMD over the current once-monthly treatment. Specific Aim 2: To create a formulation that delivers Complexa's 10-NO2-octadeca-9-enoic acid for 2 weeks. Motivation: Replicate delivery performance of osmotic pump implants current used in preclinical testing Specific Aim 3: To create a formulation for an academic PI that delivers vasotocin antisense for 10 days. Motivation: Permit the first extended evaluation of this neuropeptide's action on animal behavior in the field. Each of the formulations specified above will be designed and built in collaboration between ChroKnow Inc. and the University of Pittsburgh. Completion of each aim will yield a formulation whose in vitro release kinetics are consistent with the performance predicted by the algorithms.